Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)

2026-07-20

AI Summary

This study addresses the intersection of psychometrics and fairness research in artificial intelligence and machine learning (AI/ML), aiming to clarify the conceptual distinction between “equality” and “fairness” and to highlight the pivotal role of causal reasoning in fairness evaluation. By systematically mapping the full psychometric pipeline onto AI/ML fairness frameworks and integrating conceptual analysis with interdisciplinary comparison, the work develops a unified theoretical perspective. It not only elucidates key similarities and differences in how fairness is understood across these fields but also underscores the necessity of causal considerations for achieving genuine fairness. The resulting framework provides a clear conceptual foundation and theoretical direction for future interdisciplinary research on fairness.

Abstract

This is an invited commentary on the Psychometrika focus article "Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?" by Ying Cheng (2026, doi:10.1017/psy.2026.10110). Cheng offers a systematic comparison between long-standing test fairness and modern algorithmic fairness. Her mapping of the entire testing workflow onto the AI/ML fairness paradigm, rather than only the final selection stage, is a crucial contribution to interdisciplinary fairness research. This commentary extends her discussion by examining two conceptual issues: the distinction between equality and equity, and the role of causality in fairness research. Together, the focus article and this commentary point to directions for future fairness research across the psychometrics and AI/ML communities.

Problem

• fairness
• equality
• equity
• causality
• interdisciplinary research

Innovation

• equality vs. equity
• causality
• interdisciplinary fairness
• psychometrics
• algorithmic fairness